TY - JOUR

T1 - Efficient network immunization under limited knowledge

AU - Liu, Yangyang

AU - Sanhedrai, Hillel

AU - Dong, Gao Gao

AU - Shekhtman, Louis M.

AU - Wang, Fan

AU - Buldyrev, Sergey V.

AU - Havlin, Shlomo

N1 - Publisher Copyright:
© 2021 Oxford University Press. All rights reserved.

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Targeted immunization of centralized nodes in large-scale networks has attracted significant attention. However, in real-world scenarios, knowledge and observations of the network may be limited, thereby precluding a full assessment of the optimal nodes to immunize (or quarantine) in order to avoid epidemic spreading such as that of the current coronavirus disease (COVID-19) epidemic. Here, we study a novel immunization strategy where only n nodes are observed at a time and the most central among these n nodes is immunized.This process can globally immunize a network.We find that even for small n (?10) there is significant improvement in the immunization (quarantine), which is very close to the levels of immunization with full knowledge. We develop an analytical framework for our method and determine the critical percolation threshold pc and the size of the giant component P? for networks with arbitrary degree distributions P(k). In the limit of n we recover prior work on targeted immunization, whereas for n=1 we recover the known case of random immunization. Between these two extremes, we observe that, as n increases, pc increases quickly towards its optimal value under targeted immunization with complete information. In particular, we find a new general scaling relationship between |pc(?)?pc(n)| and n as |pc(?)?pc(n)| ? n?1exp(n). For scale-free (SF) networks, where P(k)?k , 2? 3, we find that pc has a transition from zero to nonzero when n increases from n=1 toO(logN) (whereNis the size of the network). Thus, for SF networks, having knowledge of ?logN nodes and immunizing the most optimal among them can dramatically reduce epidemic spreading.We also demonstrate our limited knowledge immunization strategy on several real-world networks and confirm that in these real networks, pc increases significantly even for small n.

AB - Targeted immunization of centralized nodes in large-scale networks has attracted significant attention. However, in real-world scenarios, knowledge and observations of the network may be limited, thereby precluding a full assessment of the optimal nodes to immunize (or quarantine) in order to avoid epidemic spreading such as that of the current coronavirus disease (COVID-19) epidemic. Here, we study a novel immunization strategy where only n nodes are observed at a time and the most central among these n nodes is immunized.This process can globally immunize a network.We find that even for small n (?10) there is significant improvement in the immunization (quarantine), which is very close to the levels of immunization with full knowledge. We develop an analytical framework for our method and determine the critical percolation threshold pc and the size of the giant component P? for networks with arbitrary degree distributions P(k). In the limit of n we recover prior work on targeted immunization, whereas for n=1 we recover the known case of random immunization. Between these two extremes, we observe that, as n increases, pc increases quickly towards its optimal value under targeted immunization with complete information. In particular, we find a new general scaling relationship between |pc(?)?pc(n)| and n as |pc(?)?pc(n)| ? n?1exp(n). For scale-free (SF) networks, where P(k)?k , 2? 3, we find that pc has a transition from zero to nonzero when n increases from n=1 toO(logN) (whereNis the size of the network). Thus, for SF networks, having knowledge of ?logN nodes and immunizing the most optimal among them can dramatically reduce epidemic spreading.We also demonstrate our limited knowledge immunization strategy on several real-world networks and confirm that in these real networks, pc increases significantly even for small n.

KW - complex networks

KW - critical phenomena

KW - network immunization

KW - percolation

UR - http://www.scopus.com/inward/record.url?scp=85092438616&partnerID=8YFLogxK

U2 - 10.1093/nsr/nwaa229

DO - 10.1093/nsr/nwaa229

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C2 - 34676088

AN - SCOPUS:85092438616

SN - 2095-5138

VL - 8

SP - nwaa229

JO - National Science Review

JF - National Science Review

IS - 1

M1 - nwaa229

ER -